Adaptive sparse representation for hyperspectral image classification

In hyerspectral remote sensing community, sparse representation based classification (SRC) is a novel concept - a testing pixel is linearly represented by labeled data, and weight coefficients are often solved by an ℓ₁-norm minimization. In this work, an extension of SRC is proposed by imposing an adaptive similarity measurement between the testing pixel and labeled data on the ℓ₁-norm penalty, named as adaptive SRC (ASRC). ASRC generates more discriminative sparse codes which can represent the testing pixel more robustly. Experimental results demonstrate that the proposed ASRC outperforms the traditional SRC-based classification.